Artificial lift systems are widely used to enhance production for reservoirs with formation pressure too low to provide enough energy to directly lift fluids to the surface. Examples of artificial lift systems include gas lift systems, hydraulic pumping units, electric submersible pumps (ESPs), progressive cavity pumps (PCPs), plunger lift systems, and sucker rod pump systems. Sucker rod pumps are currently the most commonly used artificial lift system in the industry.
There are many currently available operational tools and software programs used to monitor, evaluate, and optimize the performance of artificial lift systems. For example, pump off controllers (POCs), which gather and record periodic measurements indicative of an artificial lift system's operational status, play a significant role in monitoring the operation of artificial lift pumping systems, such as rod pumps. Additionally, POCs can be programmed to automatically shut down units if the values of torque and load deviate beyond thresholds. While POCs help reduce the amount of work required by the production and maintenance personnel operating in the field, the POCs by themselves are not sufficient. In particular, the dataset obtained by POCs poses difficult challenges due to high dimensionality, noise, and inadequate labeling.
The vast amounts of data collected (or calculated by the system) often cannot be processed in a time frame to determine what actions are needed to prevent system failures and/or improve the performance of the artificial lift well. The operating state of the artificial lift system is frequently diagnosed incorrectly, thus resulting in increased down time and reduced recovery rates. In particular, there are often interdependencies between the multiple parameters collected, which make it difficult for an automated (expert) system to evaluate the complex dynamic situations of an artificial lift system. For example, if operating parameters A, B, and C are collected where A is within normal operational conditions but B and C are outside of normal operational conditions, the system may suggest the issuance of an alert or shutdown based on the observed data signature. While there may be a need to preemptively shut down or service the artificial lift system based on the aggregate state of various operating parameters, in some cases it may be acceptable or even advantageous to continue operating the asset even if it is in a degraded mode of operation.
Expert systems, which use rule-based decisions, have been developed to better evaluate operational conditions of artificial lift systems. However, such expert systems may not perform as well as needed if a complete set of data required for making a proper diagnosis is not available. Such expert systems may also incorrectly diagnose lifting problems if the artificial lift systems are not regularly recalibrated, particularly because the parameters of operation change during the life of the well.
There is a need for automated systems, such as artificial intelligent systems that can dynamically keep track of various parameters in each and every unit, give early indications or warnings of failures, and provide suggestions on types of maintenance work required based on the knowledge acquired from previous best practices. Such a system should also display information in a useful manner such that a subject matter expert (SME) can quickly review each artificial lift system in an oil field and implement changes if necessary. Such a system would be a significant asset to the petroleum industry, especially for use in oil fields having hundreds or thousands of wells where the availability of SMEs may be limited.